发表日期：2020年11月20日 

论文名称：基于深度学习的乘用车市场预警模型研究 

作者：张立文；朱周帆；郝鸿

摘要：After developing for more than half a century, China has become the largest automobile production base and consumer market in the world. The topic about how to warn the underlying risk in automobile industry has attracted much attention, but there is no effective warning model. Based on quantile generalized autoregressive conditional heteroscedasticity model (QGARCH) and long and short term memory network model (LSTM), we build an accurate warning system for Chinese automobile market. Firstly, we build time-varying and warning zones via QGARCH model. Secondly, we predict the sales volume growth via LSTM and compare the results with other machine learning models, like support vector machine, random forest, extreme gradient boosting tree, by mean square error of prediction. Finally, we propose an effective warning system based QGARCH model and LSTM model. The results confirm that the proposed warning system can significantly improve the warning accuracy and provide valuable suggestion for enterprises.